Dataset Information

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data.

ABSTRACT: Indoor location estimation is crucial to provide context-based assistance in home environments. In this study, a method for simultaneous indoor pedestrian localization and house mapping is proposed and evaluated. The method fuses a person's movement data from an Inertial Measurement Unit (IMU) with proximity and activity-related data from Bluetooth Low-Energy (BLE) beacons deployed in the indoor environment. The person's and beacons' localization is performed simultaneously using a combination of particle and Kalman Filters. We evaluated the method using data from eight participants who performed different activities in an indoor environment. As a result, the average participant's localization error was 1.05 ± 0.44 m, and the average beacons' localization error was 0.82 ± 0.24 m. The proposed method is able to construct a map of the indoor environment by localizing the BLE beacons and simultaneously locating the person. The results obtained demonstrate that the proposed method could point to a promising roadmap towards the development of simultaneous localization and home mapping system based only on one IMU and a few BLE beacons. To the best of our knowledge, this is the first method that includes the beacons' data movement as activity-related events in a method for pedestrian Simultaneous Localization and Mapping (SLAM).

SUBMITTER: Ceron JD

PROVIDER: S-EPMC7506668 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Publications

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data.

Ceron Jesus D JD Kluge Felix F Küderle Arne A Eskofier Bjoern M BM López Diego M DM

Sensors (Basel, Switzerland) 20200822 17

Indoor location estimation is crucial to provide context-based assistance in home environments. In this study, a method for simultaneous indoor pedestrian localization and house mapping is proposed and evaluated. The method fuses a person's movement data from an Inertial Measurement Unit (IMU) with proximity and activity-related data from Bluetooth Low-Energy (BLE) beacons deployed in the indoor environment. The person's and beacons' localization is performed simultaneously using a combination o ...[more]

PMID: 32842566

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Project description:ObjectiveOver 6400 American pedestrians die annually, a figure that is currently increasing. One hypothesised reason for the increasing trend is the role of mobile technology in distracting both pedestrians and drivers. Scientists and policy-makers have attended somewhat to distracted driving, but attention to distracted pedestrian behaviour has lagged. We will evaluate Bluetooth beacon technology as a means to alert and warn pedestrians when they approach intersections, reminding them to attend to the traffic environment and cross streets safely.MethodsBluetooth beacons are small devices that broadcast information unidirectionally within a closed proximal network. We will place beacons at an intersection frequently trafficked by urban college students. From there, the beacons will transmit to an app installed on users' smartphones, signalling users to attend to their environment and cross the street safely. A cross-over trial will evaluate the app with 411 adults who frequently cross the target intersection on an urban university campus. We will monitor those participants' behaviour over three distinct time periods: (1) 3 weeks without the app being activated, (2) 3 weeks with the app activated and (3) 4 weeks without the app activated to assess retention of behaviour. Throughout the 10-week period, we will gather information to evaluate whether the intervention changes distracted pedestrian behaviour using a logistic regression to estimate the likelihood of key behavioural outcome measures and adjusting for any residual confounding. We also will test for changes in perceived risk. The trial will follow CONSORT (Consolidated Standards of Reporting Trials) statement guidelines, as modified for cross-over design studies.ConclusionIf this program proves successful, it offers exciting implications for future testing and ultimately for broad distribution to reduce distracted pedestrian behavior. We discuss issues of feasibility, acceptability and scalability.

Project description:IntroductionThe use of portable gait measurement systems in research is appealing to collect real-world data at low-cost, low participant burden, and without requirement for dedicated lab space. Most commercially available inertial measurement units (IMU's) designed for running only capture temporospatial data, the ability to capture biomechanics data such as shock and motion metrics with the RunScribe IMU makes it the closest to a lab alternative. The RunScribe system has been validated in running, however, is yet to be validated for walking.MethodQualisys motion capture, AMTI force plates, and Delsys Trigno accelerometers were used as gold standard lab measures for comparison against the RunScribe IMU. Twenty participants completed 10 footsteps per foot (20 total) measured by both systems simultaneously. Variables for validation included: Vertical Ground reaction force (GRF), instantaneous GRF rate, pronation excursion, pronation velocity, total shock, impact force, braking force. Interclass correlation (ICC) was used to determine agreement between the measurement systems, mean differences were used to evaluate group level accuracy.ResultsICC results showed moderate agreement between measurement systems when both limbs were averaged. The greatest agreement was seen for GRF rate, pronation excursion, and pronation velocity (ICC = 0.627, 0.616, 0.539), low agreement was seen for GRF, total shock, impact shock, braking shock (ICC = 0.269, 0.351, 0.244, 0.180). However mean differences show the greatest level of accuracy for GRF, GRF rate, and impact shock.DiscussionResults show mixed agreement between the RunScribe and gold standard lab measures, and varied agreement across left and right limbs. Kinematic variables showed the greatest agreement, however GRF had the lowest relative mean difference for group results. The results show acceptable levels of agreement for most variables, however further work must be done to assess the repeatability and sensitivity of the RunScribe to be applied within areas such as footwear testing and gait retraining protocols.

Dataset Information

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data.

Publications

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data.

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